Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory811.7 KiB
Average record size in memory831.2 B

Variable types

DateTime1
Unsupported1
Text4
Categorical20
Numeric4
Boolean4

Alerts

OVERALL_MIN has constant value "50.0"Constant
OVERALL_P01 has constant value "59.0"Constant
OVERALL_P05 has constant value "91.0"Constant
OVERALL_Q1 has constant value "235.0"Constant
OVERALL_MEDIAN has constant value "415.0"Constant
OVERALL_MEAN has constant value "508.58806053564336"Constant
OVERALL_Q3 has constant value "690.0"Constant
OVERALL_P95 has constant value "1224.0"Constant
OVERALL_P99 has constant value "1951.1600000000035"Constant
OVERALL_MAX has constant value "4277.0"Constant
OVERALL_IQR has constant value "455.0"Constant
OVERALL_OUTLIER_FLAG has constant value "True"Constant
VEHICLE_OUTLIER_FLAG has constant value "True"Constant
PAYMENT_OUTLIER_FLAG has constant value "True"Constant
ANY_OUTLIER_FLAG has constant value "True"Constant
Booking Status is highly overall correlated with CUSTOMER_RATING and 1 other fieldsHigh correlation
CUSTOMER_RATING is highly overall correlated with Booking StatusHigh correlation
DRIVER_RATINGS is highly overall correlated with Booking StatusHigh correlation
PAYMENT_IQR is highly overall correlated with PAYMENT_METHOD and 2 other fieldsHigh correlation
PAYMENT_METHOD is highly overall correlated with PAYMENT_IQR and 2 other fieldsHigh correlation
PAYMENT_Q1 is highly overall correlated with PAYMENT_IQR and 2 other fieldsHigh correlation
PAYMENT_Q3 is highly overall correlated with PAYMENT_IQR and 2 other fieldsHigh correlation
VEHICLE_IQR is highly overall correlated with VEHICLE_Q1 and 2 other fieldsHigh correlation
VEHICLE_Q1 is highly overall correlated with VEHICLE_IQR and 2 other fieldsHigh correlation
VEHICLE_Q3 is highly overall correlated with VEHICLE_IQR and 2 other fieldsHigh correlation
VEHICLE_TYPE is highly overall correlated with VEHICLE_IQR and 2 other fieldsHigh correlation
Booking Status is highly imbalanced (57.4%)Imbalance
CUSTOMER_ID has unique valuesUnique
TIME is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-10-16 08:03:46.789938
Analysis finished2025-10-16 08:03:49.096446
Duration2.31 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

DATE
Date

Distinct335
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2024-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-16T13:33:49.149446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:49.233543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TIME
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size47.0 KiB
Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2025-10-16T13:33:49.372828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique998 ?
Unique (%)99.8%

Sample

1st row"CNR7954315"
2nd row"CNR1798489"
3rd row"CNR8487909"
4th row"CNR5182516"
5th row"CNR7356012"
ValueCountFrequency (%)
cnr50942652
 
0.2%
cnr48029311
 
0.1%
cnr65470121
 
0.1%
cnr63414341
 
0.1%
cnr84879091
 
0.1%
cnr51825161
 
0.1%
cnr73560121
 
0.1%
cnr88491751
 
0.1%
cnr55530741
 
0.1%
cnr87159441
 
0.1%
Other values (989)989
98.9%
2025-10-16T13:33:49.538386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
N1000
 
8.3%
R1000
 
8.3%
2743
 
6.2%
5741
 
6.2%
1718
 
6.0%
9717
 
6.0%
8716
 
6.0%
4713
 
5.9%
Other values (4)2652
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
N1000
 
8.3%
R1000
 
8.3%
2743
 
6.2%
5741
 
6.2%
1718
 
6.0%
9717
 
6.0%
8716
 
6.0%
4713
 
5.9%
Other values (4)2652
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
N1000
 
8.3%
R1000
 
8.3%
2743
 
6.2%
5741
 
6.2%
1718
 
6.0%
9717
 
6.0%
8716
 
6.0%
4713
 
5.9%
Other values (4)2652
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
N1000
 
8.3%
R1000
 
8.3%
2743
 
6.2%
5741
 
6.2%
1718
 
6.0%
9717
 
6.0%
8716
 
6.0%
4713
 
5.9%
Other values (4)2652
22.1%

Booking Status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
Completed
913 
Incomplete
 
87

Length

Max length10
Median length9
Mean length9.087
Min length9

Characters and Unicode

Total characters9087
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompleted
2nd rowCompleted
3rd rowCompleted
4th rowCompleted
5th rowCompleted

Common Values

ValueCountFrequency (%)
Completed913
91.3%
Incomplete87
 
8.7%

Length

2025-10-16T13:33:49.591672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:49.638732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
completed913
91.3%
incomplete87
 
8.7%

Most occurring characters

ValueCountFrequency (%)
e2000
22.0%
o1000
11.0%
m1000
11.0%
p1000
11.0%
l1000
11.0%
t1000
11.0%
C913
10.0%
d913
10.0%
I87
 
1.0%
n87
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)9087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2000
22.0%
o1000
11.0%
m1000
11.0%
p1000
11.0%
l1000
11.0%
t1000
11.0%
C913
10.0%
d913
10.0%
I87
 
1.0%
n87
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2000
22.0%
o1000
11.0%
m1000
11.0%
p1000
11.0%
l1000
11.0%
t1000
11.0%
C913
10.0%
d913
10.0%
I87
 
1.0%
n87
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2000
22.0%
o1000
11.0%
m1000
11.0%
p1000
11.0%
l1000
11.0%
t1000
11.0%
C913
10.0%
d913
10.0%
I87
 
1.0%
n87
 
1.0%

CUSTOMER_ID
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2025-10-16T13:33:49.763252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row"CID2706299"
2nd row"CID4843078"
3rd row"CID2978596"
4th row"CID5235759"
5th row"CID5789715"
ValueCountFrequency (%)
cid27062991
 
0.1%
cid18398801
 
0.1%
cid10180091
 
0.1%
cid20272031
 
0.1%
cid29785961
 
0.1%
cid52357591
 
0.1%
cid57897151
 
0.1%
cid95391191
 
0.1%
cid26741071
 
0.1%
cid17531831
 
0.1%
Other values (990)990
99.0%
2025-10-16T13:33:49.949419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
I1000
 
8.3%
D1000
 
8.3%
2767
 
6.4%
8730
 
6.1%
9719
 
6.0%
5710
 
5.9%
1692
 
5.8%
3691
 
5.8%
Other values (4)2691
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
I1000
 
8.3%
D1000
 
8.3%
2767
 
6.4%
8730
 
6.1%
9719
 
6.0%
5710
 
5.9%
1692
 
5.8%
3691
 
5.8%
Other values (4)2691
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
I1000
 
8.3%
D1000
 
8.3%
2767
 
6.4%
8730
 
6.1%
9719
 
6.0%
5710
 
5.9%
1692
 
5.8%
3691
 
5.8%
Other values (4)2691
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
"2000
16.7%
C1000
 
8.3%
I1000
 
8.3%
D1000
 
8.3%
2767
 
6.4%
8730
 
6.1%
9719
 
6.0%
5710
 
5.9%
1692
 
5.8%
3691
 
5.8%
Other values (4)2691
22.4%

VEHICLE_TYPE
Categorical

High correlation 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
Auto
249 
Go Mini
207 
Go Sedan
177 
Bike
160 
Premier Sedan
114 
Other values (2)
93 

Length

Max length13
Median length8
Mean length6.498
Min length4

Characters and Unicode

Total characters6498
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGo Mini
2nd rowBike
3rd rowAuto
4th rowAuto
5th rowGo Mini

Common Values

ValueCountFrequency (%)
Auto249
24.9%
Go Mini207
20.7%
Go Sedan177
17.7%
Bike160
16.0%
Premier Sedan114
11.4%
eBike68
 
6.8%
Uber XL25
 
2.5%

Length

2025-10-16T13:33:50.006320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:50.055106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
go384
25.2%
sedan291
19.1%
auto249
16.3%
mini207
13.6%
bike160
10.5%
premier114
 
7.5%
ebike68
 
4.5%
uber25
 
1.6%
xl25
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e840
12.9%
i756
11.6%
o633
 
9.7%
523
 
8.0%
n498
 
7.7%
G384
 
5.9%
d291
 
4.5%
a291
 
4.5%
S291
 
4.5%
r253
 
3.9%
Other values (12)1738
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e840
12.9%
i756
11.6%
o633
 
9.7%
523
 
8.0%
n498
 
7.7%
G384
 
5.9%
d291
 
4.5%
a291
 
4.5%
S291
 
4.5%
r253
 
3.9%
Other values (12)1738
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e840
12.9%
i756
11.6%
o633
 
9.7%
523
 
8.0%
n498
 
7.7%
G384
 
5.9%
d291
 
4.5%
a291
 
4.5%
S291
 
4.5%
r253
 
3.9%
Other values (12)1738
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e840
12.9%
i756
11.6%
o633
 
9.7%
523
 
8.0%
n498
 
7.7%
G384
 
5.9%
d291
 
4.5%
a291
 
4.5%
S291
 
4.5%
r253
 
3.9%
Other values (12)1738
26.7%
Distinct174
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size66.9 KiB
2025-10-16T13:33:50.182397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length19
Mean length11.4
Min length3

Characters and Unicode

Total characters11400
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.5%

Sample

1st rowSaidulajab
2nd rowAshram
3rd rowWelcome
4th rowSubhash Nagar
5th rowIMT Manesar
ValueCountFrequency (%)
nagar94
 
5.1%
vihar55
 
3.0%
chowk45
 
2.4%
garden32
 
1.7%
road27
 
1.5%
park26
 
1.4%
gate24
 
1.3%
rohini23
 
1.3%
delhi23
 
1.3%
city23
 
1.3%
Other values (213)1466
79.8%
2025-10-16T13:33:50.366987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1857
16.3%
838
 
7.4%
r825
 
7.2%
i725
 
6.4%
h581
 
5.1%
n502
 
4.4%
e461
 
4.0%
o427
 
3.7%
t382
 
3.4%
u365
 
3.2%
Other values (48)4437
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)11400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1857
16.3%
838
 
7.4%
r825
 
7.2%
i725
 
6.4%
h581
 
5.1%
n502
 
4.4%
e461
 
4.0%
o427
 
3.7%
t382
 
3.4%
u365
 
3.2%
Other values (48)4437
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1857
16.3%
838
 
7.4%
r825
 
7.2%
i725
 
6.4%
h581
 
5.1%
n502
 
4.4%
e461
 
4.0%
o427
 
3.7%
t382
 
3.4%
u365
 
3.2%
Other values (48)4437
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1857
16.3%
838
 
7.4%
r825
 
7.2%
i725
 
6.4%
h581
 
5.1%
n502
 
4.4%
e461
 
4.0%
o427
 
3.7%
t382
 
3.4%
u365
 
3.2%
Other values (48)4437
38.9%
Distinct176
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size66.9 KiB
2025-10-16T13:33:50.489476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length20
Mean length11.359
Min length3

Characters and Unicode

Total characters11359
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowNetaji Subhash Place
2nd rowPatel Chowk
3rd rowJama Masjid
4th rowLaxmi Nagar
5th rowSarojini Nagar
ValueCountFrequency (%)
nagar102
 
5.6%
vihar58
 
3.2%
chowk52
 
2.8%
sector34
 
1.9%
park30
 
1.6%
noida30
 
1.6%
gurgaon25
 
1.4%
place25
 
1.4%
rohini24
 
1.3%
city21
 
1.1%
Other values (214)1427
78.1%
2025-10-16T13:33:50.676696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1866
16.4%
828
 
7.3%
r824
 
7.3%
i744
 
6.5%
h581
 
5.1%
n509
 
4.5%
e474
 
4.2%
o444
 
3.9%
t390
 
3.4%
u334
 
2.9%
Other values (48)4365
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)11359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1866
16.4%
828
 
7.3%
r824
 
7.3%
i744
 
6.5%
h581
 
5.1%
n509
 
4.5%
e474
 
4.2%
o444
 
3.9%
t390
 
3.4%
u334
 
2.9%
Other values (48)4365
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1866
16.4%
828
 
7.3%
r824
 
7.3%
i744
 
6.5%
h581
 
5.1%
n509
 
4.5%
e474
 
4.2%
o444
 
3.9%
t390
 
3.4%
u334
 
2.9%
Other values (48)4365
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1866
16.4%
828
 
7.3%
r824
 
7.3%
i744
 
6.5%
h581
 
5.1%
n509
 
4.5%
e474
 
4.2%
o444
 
3.9%
t390
 
3.4%
u334
 
2.9%
Other values (48)4365
38.4%

BOOKING_VALUE_NUM
Real number (ℝ)

Distinct729
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2303.648
Minimum1671
Maximum4277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-16T13:33:50.740657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1671
5-th percentile1691
Q11864.25
median2175.5
Q32471
95-th percentile3553.05
Maximum4277
Range2606
Interquartile range (IQR)606.75

Descriptive statistics

Standard deviation571.1835
Coefficient of variation (CV)0.24794739
Kurtosis1.0115398
Mean2303.648
Median Absolute Deviation (MAD)302.5
Skewness1.2487874
Sum2303648
Variance326250.59
MonotonicityDecreasing
2025-10-16T13:33:50.817945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17585
 
0.5%
19685
 
0.5%
23925
 
0.5%
16745
 
0.5%
16775
 
0.5%
16825
 
0.5%
17194
 
0.4%
23954
 
0.4%
17074
 
0.4%
16964
 
0.4%
Other values (719)954
95.4%
ValueCountFrequency (%)
16712
 
0.2%
16724
0.4%
16731
 
0.1%
16745
0.5%
16752
 
0.2%
16775
0.5%
16783
0.3%
16791
 
0.1%
16803
0.3%
16812
 
0.2%
ValueCountFrequency (%)
42771
0.1%
42281
0.1%
42201
0.1%
42021
0.1%
41331
0.1%
41091
0.1%
40931
0.1%
40881
0.1%
40441
0.1%
40321
0.1%

RIDE_DISTANCE_NUM
Real number (ℝ)

Distinct899
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.34904
Minimum1
Maximum49.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-16T13:33:50.895871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.9185
Q113.2875
median23.925
Q337.56
95-th percentile47.7515
Maximum49.86
Range48.86
Interquartile range (IQR)24.2725

Descriptive statistics

Standard deviation14.16158
Coefficient of variation (CV)0.55866338
Kurtosis-1.2420524
Mean25.34904
Median Absolute Deviation (MAD)12.375
Skewness0.083731785
Sum25349.04
Variance200.55036
MonotonicityNot monotonic
2025-10-16T13:33:50.978773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.034
 
0.4%
27.613
 
0.3%
17.453
 
0.3%
3.843
 
0.3%
2.153
 
0.3%
29.63
 
0.3%
16.763
 
0.3%
9.982
 
0.2%
31.872
 
0.2%
46.322
 
0.2%
Other values (889)972
97.2%
ValueCountFrequency (%)
11
 
0.1%
1.411
 
0.1%
1.831
 
0.1%
1.891
 
0.1%
2.021
 
0.1%
2.081
 
0.1%
2.153
0.3%
2.181
 
0.1%
2.291
 
0.1%
2.31
 
0.1%
ValueCountFrequency (%)
49.861
0.1%
49.841
0.1%
49.821
0.1%
49.791
0.1%
49.781
0.1%
49.711
0.1%
49.671
0.1%
49.551
0.1%
49.541
0.1%
49.461
0.1%

DRIVER_RATINGS
Categorical

High correlation 

Distinct22
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size58.8 KiB
4.3
163 
4.2
129 
null
87 
4.6
85 
4.1
69 
Other values (17)
467 

Length

Max length4
Median length3
Mean length3.087
Min length3

Characters and Unicode

Total characters3087
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.7
2nd row4.4
3rd row3.6
4th row4.9
5th row3.9

Common Values

ValueCountFrequency (%)
4.3163
16.3%
4.2129
12.9%
null87
 
8.7%
4.685
 
8.5%
4.169
 
6.9%
4.465
 
6.5%
4.547
 
4.7%
3.845
 
4.5%
4.741
 
4.1%
4.941
 
4.1%
Other values (12)228
22.8%

Length

2025-10-16T13:33:51.048418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4.3163
16.3%
4.2129
12.9%
null87
 
8.7%
4.685
 
8.5%
4.169
 
6.9%
4.465
 
6.5%
4.547
 
4.7%
3.845
 
4.5%
4.741
 
4.1%
4.941
 
4.1%
Other values (12)228
22.8%

Most occurring characters

ValueCountFrequency (%)
.913
29.6%
4767
24.8%
3377
12.2%
l174
 
5.6%
2143
 
4.6%
6103
 
3.3%
n87
 
2.8%
u87
 
2.8%
180
 
2.6%
980
 
2.6%
Other values (4)276
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.913
29.6%
4767
24.8%
3377
12.2%
l174
 
5.6%
2143
 
4.6%
6103
 
3.3%
n87
 
2.8%
u87
 
2.8%
180
 
2.6%
980
 
2.6%
Other values (4)276
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.913
29.6%
4767
24.8%
3377
12.2%
l174
 
5.6%
2143
 
4.6%
6103
 
3.3%
n87
 
2.8%
u87
 
2.8%
180
 
2.6%
980
 
2.6%
Other values (4)276
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.913
29.6%
4767
24.8%
3377
12.2%
l174
 
5.6%
2143
 
4.6%
6103
 
3.3%
n87
 
2.8%
u87
 
2.8%
180
 
2.6%
980
 
2.6%
Other values (4)276
 
8.9%

CUSTOMER_RATING
Categorical

High correlation 

Distinct22
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size58.8 KiB
4.9
124 
4.3
118 
4.2
108 
4.6
102 
null
87 
Other values (17)
461 

Length

Max length4
Median length3
Mean length3.087
Min length3

Characters and Unicode

Total characters3087
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row4.9
2nd row5.0
3rd row4.8
4th row4.7
5th row4.3

Common Values

ValueCountFrequency (%)
4.9124
12.4%
4.3118
11.8%
4.2108
10.8%
4.6102
10.2%
null87
8.7%
4.567
6.7%
4.764
6.4%
4.861
 
6.1%
5.057
 
5.7%
4.455
 
5.5%
Other values (12)157
15.7%

Length

2025-10-16T13:33:51.104302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4.9124
12.4%
4.3118
11.8%
4.2108
10.8%
4.6102
10.2%
null87
8.7%
4.567
6.7%
4.764
6.4%
4.861
 
6.1%
5.057
 
5.7%
4.455
 
5.5%
Other values (12)157
15.7%

Most occurring characters

ValueCountFrequency (%)
.913
29.6%
4822
26.6%
3225
 
7.3%
l174
 
5.6%
9136
 
4.4%
5127
 
4.1%
6117
 
3.8%
2112
 
3.6%
n87
 
2.8%
u87
 
2.8%
Other values (4)287
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.913
29.6%
4822
26.6%
3225
 
7.3%
l174
 
5.6%
9136
 
4.4%
5127
 
4.1%
6117
 
3.8%
2112
 
3.6%
n87
 
2.8%
u87
 
2.8%
Other values (4)287
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.913
29.6%
4822
26.6%
3225
 
7.3%
l174
 
5.6%
9136
 
4.4%
5127
 
4.1%
6117
 
3.8%
2112
 
3.6%
n87
 
2.8%
u87
 
2.8%
Other values (4)287
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.913
29.6%
4822
26.6%
3225
 
7.3%
l174
 
5.6%
9136
 
4.4%
5127
 
4.1%
6117
 
3.8%
2112
 
3.6%
n87
 
2.8%
u87
 
2.8%
Other values (4)287
 
9.3%

PAYMENT_METHOD
Categorical

High correlation 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size61.2 KiB
UPI
463 
Cash
237 
Uber Wallet
114 
Credit Card
99 
Debit Card
87 

Length

Max length11
Median length10
Mean length5.55
Min length3

Characters and Unicode

Total characters5550
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUPI
2nd rowDebit Card
3rd rowUPI
4th rowUber Wallet
5th rowCash

Common Values

ValueCountFrequency (%)
UPI463
46.3%
Cash237
23.7%
Uber Wallet114
 
11.4%
Credit Card99
 
9.9%
Debit Card87
 
8.7%

Length

2025-10-16T13:33:51.169432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.307377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
upi463
35.6%
cash237
18.2%
card186
14.3%
uber114
 
8.8%
wallet114
 
8.8%
credit99
 
7.6%
debit87
 
6.7%

Most occurring characters

ValueCountFrequency (%)
U577
10.4%
a537
9.7%
C522
9.4%
P463
 
8.3%
I463
 
8.3%
e414
 
7.5%
r399
 
7.2%
300
 
5.4%
t300
 
5.4%
d285
 
5.1%
Other values (7)1290
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U577
10.4%
a537
9.7%
C522
9.4%
P463
 
8.3%
I463
 
8.3%
e414
 
7.5%
r399
 
7.2%
300
 
5.4%
t300
 
5.4%
d285
 
5.1%
Other values (7)1290
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U577
10.4%
a537
9.7%
C522
9.4%
P463
 
8.3%
I463
 
8.3%
e414
 
7.5%
r399
 
7.2%
300
 
5.4%
t300
 
5.4%
d285
 
5.1%
Other values (7)1290
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U577
10.4%
a537
9.7%
C522
9.4%
P463
 
8.3%
I463
 
8.3%
e414
 
7.5%
r399
 
7.2%
300
 
5.4%
t300
 
5.4%
d285
 
5.1%
Other values (7)1290
23.2%

OVERALL_MIN
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
50.0
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50.0
2nd row50.0
3rd row50.0
4th row50.0
5th row50.0

Common Values

ValueCountFrequency (%)
50.01000
100.0%

Length

2025-10-16T13:33:51.367972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.399623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
50.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
02000
50.0%
51000
25.0%
.1000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02000
50.0%
51000
25.0%
.1000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02000
50.0%
51000
25.0%
.1000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02000
50.0%
51000
25.0%
.1000
25.0%

OVERALL_P01
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
59.0
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row59.0
2nd row59.0
3rd row59.0
4th row59.0
5th row59.0

Common Values

ValueCountFrequency (%)
59.01000
100.0%

Length

2025-10-16T13:33:51.441400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.472652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
59.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
51000
25.0%
91000
25.0%
.1000
25.0%
01000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51000
25.0%
91000
25.0%
.1000
25.0%
01000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51000
25.0%
91000
25.0%
.1000
25.0%
01000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51000
25.0%
91000
25.0%
.1000
25.0%
01000
25.0%

OVERALL_P05
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
91.0
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row91.0
2nd row91.0
3rd row91.0
4th row91.0
5th row91.0

Common Values

ValueCountFrequency (%)
91.01000
100.0%

Length

2025-10-16T13:33:51.513510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.545253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
91.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
91000
25.0%
11000
25.0%
.1000
25.0%
01000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
91000
25.0%
11000
25.0%
.1000
25.0%
01000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
91000
25.0%
11000
25.0%
.1000
25.0%
01000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
91000
25.0%
11000
25.0%
.1000
25.0%
01000
25.0%

OVERALL_Q1
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
235.0
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row235.0
2nd row235.0
3rd row235.0
4th row235.0
5th row235.0

Common Values

ValueCountFrequency (%)
235.01000
100.0%

Length

2025-10-16T13:33:51.586337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.617574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
235.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
21000
20.0%
31000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21000
20.0%
31000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21000
20.0%
31000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21000
20.0%
31000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

OVERALL_MEDIAN
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
415.0
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415.0
2nd row415.0
3rd row415.0
4th row415.0
5th row415.0

Common Values

ValueCountFrequency (%)
415.01000
100.0%

Length

2025-10-16T13:33:51.659269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.690648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
415.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
41000
20.0%
11000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41000
20.0%
11000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41000
20.0%
11000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41000
20.0%
11000
20.0%
51000
20.0%
.1000
20.0%
01000
20.0%

OVERALL_MEAN
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
508.58806053564336
1000 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters18000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row508.58806053564336
2nd row508.58806053564336
3rd row508.58806053564336
4th row508.58806053564336
5th row508.58806053564336

Common Values

ValueCountFrequency (%)
508.588060535643361000
100.0%

Length

2025-10-16T13:33:51.732483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.764396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
508.588060535643361000
100.0%

Most occurring characters

ValueCountFrequency (%)
54000
22.2%
03000
16.7%
83000
16.7%
63000
16.7%
33000
16.7%
.1000
 
5.6%
41000
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)18000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
54000
22.2%
03000
16.7%
83000
16.7%
63000
16.7%
33000
16.7%
.1000
 
5.6%
41000
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
54000
22.2%
03000
16.7%
83000
16.7%
63000
16.7%
33000
16.7%
.1000
 
5.6%
41000
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
54000
22.2%
03000
16.7%
83000
16.7%
63000
16.7%
33000
16.7%
.1000
 
5.6%
41000
 
5.6%

OVERALL_Q3
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
690.0
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row690.0
2nd row690.0
3rd row690.0
4th row690.0
5th row690.0

Common Values

ValueCountFrequency (%)
690.01000
100.0%

Length

2025-10-16T13:33:51.806451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.839303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
690.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
02000
40.0%
61000
20.0%
91000
20.0%
.1000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02000
40.0%
61000
20.0%
91000
20.0%
.1000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02000
40.0%
61000
20.0%
91000
20.0%
.1000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02000
40.0%
61000
20.0%
91000
20.0%
.1000
20.0%

OVERALL_P95
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
1224.0
1000 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1224.0
2nd row1224.0
3rd row1224.0
4th row1224.0
5th row1224.0

Common Values

ValueCountFrequency (%)
1224.01000
100.0%

Length

2025-10-16T13:33:51.880247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.911697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1224.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
22000
33.3%
11000
16.7%
41000
16.7%
.1000
16.7%
01000
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22000
33.3%
11000
16.7%
41000
16.7%
.1000
16.7%
01000
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22000
33.3%
11000
16.7%
41000
16.7%
.1000
16.7%
01000
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22000
33.3%
11000
16.7%
41000
16.7%
.1000
16.7%
01000
16.7%

OVERALL_P99
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
1951.1600000000035
1000 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters18000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1951.1600000000035
2nd row1951.1600000000035
3rd row1951.1600000000035
4th row1951.1600000000035
5th row1951.1600000000035

Common Values

ValueCountFrequency (%)
1951.16000000000351000
100.0%

Length

2025-10-16T13:33:51.953849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:51.985723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1951.16000000000351000
100.0%

Most occurring characters

ValueCountFrequency (%)
09000
50.0%
13000
 
16.7%
52000
 
11.1%
91000
 
5.6%
.1000
 
5.6%
61000
 
5.6%
31000
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)18000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09000
50.0%
13000
 
16.7%
52000
 
11.1%
91000
 
5.6%
.1000
 
5.6%
61000
 
5.6%
31000
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09000
50.0%
13000
 
16.7%
52000
 
11.1%
91000
 
5.6%
.1000
 
5.6%
61000
 
5.6%
31000
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09000
50.0%
13000
 
16.7%
52000
 
11.1%
91000
 
5.6%
.1000
 
5.6%
61000
 
5.6%
31000
 
5.6%

OVERALL_MAX
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
4277.0
1000 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4277.0
2nd row4277.0
3rd row4277.0
4th row4277.0
5th row4277.0

Common Values

ValueCountFrequency (%)
4277.01000
100.0%

Length

2025-10-16T13:33:52.027395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:52.059627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4277.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
72000
33.3%
41000
16.7%
21000
16.7%
.1000
16.7%
01000
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
72000
33.3%
41000
16.7%
21000
16.7%
.1000
16.7%
01000
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
72000
33.3%
41000
16.7%
21000
16.7%
.1000
16.7%
01000
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
72000
33.3%
41000
16.7%
21000
16.7%
.1000
16.7%
01000
16.7%

OVERALL_IQR
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
455.0
1000 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row455.0
2nd row455.0
3rd row455.0
4th row455.0
5th row455.0

Common Values

ValueCountFrequency (%)
455.01000
100.0%

Length

2025-10-16T13:33:52.100580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:52.131856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
455.01000
100.0%

Most occurring characters

ValueCountFrequency (%)
52000
40.0%
41000
20.0%
.1000
20.0%
01000
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
52000
40.0%
41000
20.0%
.1000
20.0%
01000
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
52000
40.0%
41000
20.0%
.1000
20.0%
01000
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
52000
40.0%
41000
20.0%
.1000
20.0%
01000
20.0%

VEHICLE_Q1
Categorical

High correlation 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
236.0
363 
234.0
232 
238.0
177 
235.0
160 
231.0
68 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row234.0
2nd row235.0
3rd row236.0
4th row236.0
5th row234.0

Common Values

ValueCountFrequency (%)
236.0363
36.3%
234.0232
23.2%
238.0177
17.7%
235.0160
16.0%
231.068
 
6.8%

Length

2025-10-16T13:33:52.173642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:52.215910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
236.0363
36.3%
234.0232
23.2%
238.0177
17.7%
235.0160
16.0%
231.068
 
6.8%

Most occurring characters

ValueCountFrequency (%)
21000
20.0%
31000
20.0%
.1000
20.0%
01000
20.0%
6363
 
7.3%
4232
 
4.6%
8177
 
3.5%
5160
 
3.2%
168
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21000
20.0%
31000
20.0%
.1000
20.0%
01000
20.0%
6363
 
7.3%
4232
 
4.6%
8177
 
3.5%
5160
 
3.2%
168
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21000
20.0%
31000
20.0%
.1000
20.0%
01000
20.0%
6363
 
7.3%
4232
 
4.6%
8177
 
3.5%
5160
 
3.2%
168
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21000
20.0%
31000
20.0%
.1000
20.0%
01000
20.0%
6363
 
7.3%
4232
 
4.6%
8177
 
3.5%
5160
 
3.2%
168
 
1.4%

VEHICLE_Q3
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean689.857
Minimum676
Maximum701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-16T13:33:52.264101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum676
5-th percentile683
Q1686
median686
Q3693.5
95-th percentile701
Maximum701
Range25
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.238868
Coefficient of variation (CV)0.009043712
Kurtosis-0.39676251
Mean689.857
Median Absolute Deviation (MAD)3
Skewness0.57008456
Sum689857
Variance38.923474
MonotonicityNot monotonic
2025-10-16T13:33:52.313139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
686456
45.6%
701177
 
17.7%
693.5160
 
16.0%
690114
 
11.4%
68368
 
6.8%
67625
 
2.5%
ValueCountFrequency (%)
67625
 
2.5%
68368
 
6.8%
686456
45.6%
690114
 
11.4%
693.5160
 
16.0%
701177
 
17.7%
ValueCountFrequency (%)
701177
 
17.7%
693.5160
 
16.0%
690114
 
11.4%
686456
45.6%
68368
 
6.8%
67625
 
2.5%

VEHICLE_IQR
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.467
Minimum442
Maximum463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-10-16T13:33:52.359065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum442
5-th percentile450
Q1450
median452
Q3458.5
95-th percentile463
Maximum463
Range21
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.1057791
Coefficient of variation (CV)0.011234653
Kurtosis-0.49886289
Mean454.467
Median Absolute Deviation (MAD)2
Skewness0.34429435
Sum454467
Variance26.06898
MonotonicityNot monotonic
2025-10-16T13:33:52.408682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
452275
27.5%
450249
24.9%
463177
17.7%
458.5160
16.0%
454114
11.4%
44225
 
2.5%
ValueCountFrequency (%)
44225
 
2.5%
450249
24.9%
452275
27.5%
454114
11.4%
458.5160
16.0%
463177
17.7%
ValueCountFrequency (%)
463177
17.7%
458.5160
16.0%
454114
11.4%
452275
27.5%
450249
24.9%
44225
 
2.5%

PAYMENT_Q1
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
233.0
463 
234.0
351 
243.75
99 
240.0
87 

Length

Max length6
Median length5
Mean length5.099
Min length5

Characters and Unicode

Total characters5099
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row233.0
2nd row240.0
3rd row233.0
4th row234.0
5th row234.0

Common Values

ValueCountFrequency (%)
233.0463
46.3%
234.0351
35.1%
243.7599
 
9.9%
240.087
 
8.7%

Length

2025-10-16T13:33:52.466140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:52.509642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
233.0463
46.3%
234.0351
35.1%
243.7599
 
9.9%
240.087
 
8.7%

Most occurring characters

ValueCountFrequency (%)
31376
27.0%
21000
19.6%
.1000
19.6%
0988
19.4%
4537
 
10.5%
799
 
1.9%
599
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)5099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31376
27.0%
21000
19.6%
.1000
19.6%
0988
19.4%
4537
 
10.5%
799
 
1.9%
599
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31376
27.0%
21000
19.6%
.1000
19.6%
0988
19.4%
4537
 
10.5%
799
 
1.9%
599
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31376
27.0%
21000
19.6%
.1000
19.6%
0988
19.4%
4537
 
10.5%
799
 
1.9%
599
 
1.9%

PAYMENT_Q3
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
694.0
463 
683.0
336 
690.0
114 
692.0
87 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row694.0
2nd row692.0
3rd row694.0
4th row690.0
5th row683.0

Common Values

ValueCountFrequency (%)
694.0463
46.3%
683.0336
33.6%
690.0114
 
11.4%
692.087
 
8.7%

Length

2025-10-16T13:33:52.565603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:52.606840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
694.0463
46.3%
683.0336
33.6%
690.0114
 
11.4%
692.087
 
8.7%

Most occurring characters

ValueCountFrequency (%)
01114
22.3%
61000
20.0%
.1000
20.0%
9664
13.3%
4463
9.3%
8336
 
6.7%
3336
 
6.7%
287
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01114
22.3%
61000
20.0%
.1000
20.0%
9664
13.3%
4463
9.3%
8336
 
6.7%
3336
 
6.7%
287
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01114
22.3%
61000
20.0%
.1000
20.0%
9664
13.3%
4463
9.3%
8336
 
6.7%
3336
 
6.7%
287
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01114
22.3%
61000
20.0%
.1000
20.0%
9664
13.3%
4463
9.3%
8336
 
6.7%
3336
 
6.7%
287
 
1.7%

PAYMENT_IQR
Categorical

High correlation 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
461.0
463 
449.0
237 
456.0
114 
439.25
99 
452.0
87 

Length

Max length6
Median length5
Mean length5.099
Min length5

Characters and Unicode

Total characters5099
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row461.0
2nd row452.0
3rd row461.0
4th row456.0
5th row449.0

Common Values

ValueCountFrequency (%)
461.0463
46.3%
449.0237
23.7%
456.0114
 
11.4%
439.2599
 
9.9%
452.087
 
8.7%

Length

2025-10-16T13:33:52.664319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-16T13:33:52.709050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
461.0463
46.3%
449.0237
23.7%
456.0114
 
11.4%
439.2599
 
9.9%
452.087
 
8.7%

Most occurring characters

ValueCountFrequency (%)
41237
24.3%
.1000
19.6%
0901
17.7%
6577
11.3%
1463
 
9.1%
9336
 
6.6%
5300
 
5.9%
2186
 
3.6%
399
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)5099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41237
24.3%
.1000
19.6%
0901
17.7%
6577
11.3%
1463
 
9.1%
9336
 
6.6%
5300
 
5.9%
2186
 
3.6%
399
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41237
24.3%
.1000
19.6%
0901
17.7%
6577
11.3%
1463
 
9.1%
9336
 
6.6%
5300
 
5.9%
2186
 
3.6%
399
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41237
24.3%
.1000
19.6%
0901
17.7%
6577
11.3%
1463
 
9.1%
9336
 
6.6%
5300
 
5.9%
2186
 
3.6%
399
 
1.9%

OVERALL_OUTLIER_FLAG
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
1000 
ValueCountFrequency (%)
True1000
100.0%
2025-10-16T13:33:52.745765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

VEHICLE_OUTLIER_FLAG
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
1000 
ValueCountFrequency (%)
True1000
100.0%
2025-10-16T13:33:52.765728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

PAYMENT_OUTLIER_FLAG
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
1000 
ValueCountFrequency (%)
True1000
100.0%
2025-10-16T13:33:52.784655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ANY_OUTLIER_FLAG
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
1000 
ValueCountFrequency (%)
True1000
100.0%
2025-10-16T13:33:52.803732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-10-16T13:33:48.483454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:47.645029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:47.934696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.208424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.547633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:47.733271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.002650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.276976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.614386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:47.800990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.071768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.347651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.680030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:47.871343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.142691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-16T13:33:48.416032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-16T13:33:52.849484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BOOKING_VALUE_NUMBooking StatusCUSTOMER_RATINGDRIVER_RATINGSPAYMENT_IQRPAYMENT_METHODPAYMENT_Q1PAYMENT_Q3RIDE_DISTANCE_NUMVEHICLE_IQRVEHICLE_Q1VEHICLE_Q3VEHICLE_TYPE
BOOKING_VALUE_NUM1.0000.0000.0530.0640.0340.0340.0470.049-0.0270.0050.000-0.0210.016
Booking Status0.0001.0000.9900.9900.0000.0000.0000.0000.3470.0000.0000.0000.000
CUSTOMER_RATING0.0530.9901.0000.2210.0000.0000.0000.0000.1230.0220.0450.0090.000
DRIVER_RATINGS0.0640.9900.2211.0000.0000.0000.0000.0000.1300.0000.0000.0000.010
PAYMENT_IQR0.0340.0000.0000.0001.0001.0000.9990.9990.0000.0000.0000.0000.000
PAYMENT_METHOD0.0340.0000.0000.0001.0001.0000.9990.9990.0000.0000.0000.0000.000
PAYMENT_Q10.0470.0000.0000.0000.9990.9991.0000.8350.0290.0000.0000.0000.000
PAYMENT_Q30.0490.0000.0000.0000.9990.9990.8351.0000.0000.0000.0000.0000.000
RIDE_DISTANCE_NUM-0.0270.3470.1230.1300.0000.0000.0290.0001.0000.0410.0000.0220.000
VEHICLE_IQR0.0050.0000.0220.0000.0000.0000.0000.0000.0411.0000.8690.8870.999
VEHICLE_Q10.0000.0000.0450.0000.0000.0000.0000.0000.0000.8691.0000.8940.999
VEHICLE_Q3-0.0210.0000.0090.0000.0000.0000.0000.0000.0220.8870.8941.0000.999
VEHICLE_TYPE0.0160.0000.0000.0100.0000.0000.0000.0000.0000.9990.9990.9991.000

Missing values

2025-10-16T13:33:48.835632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-16T13:33:48.999592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DATETIMEBOOKING_IDBooking StatusCUSTOMER_IDVEHICLE_TYPEPICKUP_LOCATIONDROP_LOCATIONBOOKING_VALUE_NUMRIDE_DISTANCE_NUMDRIVER_RATINGSCUSTOMER_RATINGPAYMENT_METHODOVERALL_MINOVERALL_P01OVERALL_P05OVERALL_Q1OVERALL_MEDIANOVERALL_MEANOVERALL_Q3OVERALL_P95OVERALL_P99OVERALL_MAXOVERALL_IQRVEHICLE_Q1VEHICLE_Q3VEHICLE_IQRPAYMENT_Q1PAYMENT_Q3PAYMENT_IQROVERALL_OUTLIER_FLAGVEHICLE_OUTLIER_FLAGPAYMENT_OUTLIER_FLAGANY_OUTLIER_FLAG
02024-10-0505:09:13"CNR7954315"Completed"CID2706299"Go MiniSaidulajabNetaji Subhash Place4277.08.664.74.9UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0234.0686.0452.0233.0694.0461.0TrueTrueTrueTrue
12024-08-2518:35:03"CNR1798489"Completed"CID4843078"BikeAshramPatel Chowk4228.011.734.45.0Debit Card50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0235.0693.5458.5240.0692.0452.0TrueTrueTrueTrue
22024-05-1510:11:36"CNR8487909"Completed"CID2978596"AutoWelcomeJama Masjid4220.010.113.64.8UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0236.0686.0450.0233.0694.0461.0TrueTrueTrueTrue
32024-08-2517:02:46"CNR5182516"Completed"CID5235759"AutoSubhash NagarLaxmi Nagar4202.04.624.94.7Uber Wallet50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0236.0686.0450.0234.0690.0456.0TrueTrueTrueTrue
42024-07-1317:56:05"CNR7356012"Completed"CID5789715"Go MiniIMT ManesarSarojini Nagar4133.025.663.94.3Cash50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0234.0686.0452.0234.0683.0449.0TrueTrueTrueTrue
52024-09-1416:20:32"CNR8849175"Completed"CID9539119"Go MiniAshok ViharBasai Dhankot4109.036.813.74.7UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0234.0686.0452.0233.0694.0461.0TrueTrueTrueTrue
62024-12-2121:51:46"CNR5553074"Completed"CID2674107"eBikeGTB NagarNarsinghpur4093.020.853.53.1UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0231.0683.0452.0233.0694.0461.0TrueTrueTrueTrue
72024-07-2122:42:15"CNR8715944"Completed"CID1753183"Go SedanKarol BaghPitampura4088.046.364.64.3UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0238.0701.0463.0233.0694.0461.0TrueTrueTrueTrue
82024-04-1415:23:28"CNR8875064"Completed"CID6777086"Go SedanDwarka MorSeelampur4044.04.975.04.2UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0238.0701.0463.0233.0694.0461.0TrueTrueTrueTrue
92024-04-3008:39:24"CNR1017046"Completed"CID4542328"AutoPaschim ViharMalviya Nagar4032.032.114.63.3UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0236.0686.0450.0233.0694.0461.0TrueTrueTrueTrue
DATETIMEBOOKING_IDBooking StatusCUSTOMER_IDVEHICLE_TYPEPICKUP_LOCATIONDROP_LOCATIONBOOKING_VALUE_NUMRIDE_DISTANCE_NUMDRIVER_RATINGSCUSTOMER_RATINGPAYMENT_METHODOVERALL_MINOVERALL_P01OVERALL_P05OVERALL_Q1OVERALL_MEDIANOVERALL_MEANOVERALL_Q3OVERALL_P95OVERALL_P99OVERALL_MAXOVERALL_IQRVEHICLE_Q1VEHICLE_Q3VEHICLE_IQRPAYMENT_Q1PAYMENT_Q3PAYMENT_IQROVERALL_OUTLIER_FLAGVEHICLE_OUTLIER_FLAGPAYMENT_OUTLIER_FLAGANY_OUTLIER_FLAG
9902024-11-2819:30:50"CNR7541856"Completed"CID5004315"Go SedanIGI AirportKarol Bagh1674.049.843.73.6UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0238.0701.0463.0233.00694.0461.00TrueTrueTrueTrue
9912024-09-2910:31:19"CNR5846254"Completed"CID3213990"Go SedanSamaypur BadliHauz Rani1674.025.813.75.0Debit Card50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0238.0701.0463.0240.00692.0452.00TrueTrueTrueTrue
9922024-12-1420:24:12"CNR3790517"Completed"CID9521728"BikeOkhlaWelcome1674.021.074.34.1Cash50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0235.0693.5458.5234.00683.0449.00TrueTrueTrueTrue
9932024-06-2921:33:41"CNR1877105"Completed"CID7813194"BikeDLF City CourtAmbience Mall1673.014.854.34.3UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0235.0693.5458.5233.00694.0461.00TrueTrueTrueTrue
9942024-02-0411:21:14"CNR7283103"Completed"CID1135104"Premier SedanShastri ParkLajpat Nagar1672.041.904.34.5Uber Wallet50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0236.0690.0454.0234.00690.0456.00TrueTrueTrueTrue
9952024-04-0608:06:05"CNR2931890"Completed"CID5754568"Go SedanRohini EastQutub Minar1672.024.964.34.2Uber Wallet50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0238.0701.0463.0234.00690.0456.00TrueTrueTrueTrue
9962024-03-1604:53:22"CNR9690460"Completed"CID9707251"Go MiniOld GurgaonDelhi Gate1672.019.034.14.2Credit Card50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0234.0686.0452.0243.75683.0439.25TrueTrueTrueTrue
9972024-01-0713:55:13"CNR1593742"Completed"CID9989148"Premier SedanBhikaji Cama PlaceAkshardham1672.033.553.64.9UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0236.0690.0454.0233.00694.0461.00TrueTrueTrueTrue
9982024-12-0720:58:59"CNR9687817"Completed"CID2972346"Go SedanPataudi ChowkSohna Road1671.046.324.34.9UPI50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0238.0701.0463.0233.00694.0461.00TrueTrueTrueTrue
9992024-07-0721:58:50"CNR4742995"Completed"CID6927282"Go MiniNawadaKashmere Gate1671.033.654.03.6Uber Wallet50.059.091.0235.0415.0508.588061690.01224.01951.164277.0455.0234.0686.0452.0234.00690.0456.00TrueTrueTrueTrue